{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>body {\n",
       "    margin: 0;\n",
       "    font-family: Helvetica;\n",
       "}\n",
       "table.dataframe {\n",
       "    border-collapse: collapse;\n",
       "    border: none;\n",
       "}\n",
       "table.dataframe tr {\n",
       "    border: none;\n",
       "}\n",
       "table.dataframe td, table.dataframe th {\n",
       "    margin: 0;\n",
       "    border: 1px solid white;\n",
       "    padding-left: 0.25em;\n",
       "    padding-right: 0.25em;\n",
       "}\n",
       "table.dataframe th:not(:empty) {\n",
       "    background-color: #fec;\n",
       "    text-align: left;\n",
       "    font-weight: normal;\n",
       "}\n",
       "table.dataframe tr:nth-child(2) th:empty {\n",
       "    border-left: none;\n",
       "    border-right: 1px dashed #888;\n",
       "}\n",
       "table.dataframe td {\n",
       "    border: 2px solid #ccf;\n",
       "    background-color: #f4f4ff;\n",
       "}\n",
       "h3 {\n",
       "    color: white;\n",
       "    background-color: black;\n",
       "    padding: 0.5em;\n",
       "}\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "css = open('style-table.css').read() + open('style-notebook.css').read()\n",
    "HTML('<style>{}</style>'.format(css))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tomorrow Ends at Dawn</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brothers of the West</td>\n",
       "      <td>1937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nemo</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pereezd</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bad for Business</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   title  year\n",
       "0  Tomorrow Ends at Dawn  2002\n",
       "1   Brothers of the West  1937\n",
       "2                   Nemo  1984\n",
       "3                Pereezd  2014\n",
       "4       Bad for Business  2007"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles = pd.read_csv('data/titles.csv')\n",
    "titles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>character</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Suuri illusioni</td>\n",
       "      <td>1985</td>\n",
       "      <td>Homo $</td>\n",
       "      <td>actor</td>\n",
       "      <td>Guests</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gangsta Rap: The Glockumentary</td>\n",
       "      <td>2007</td>\n",
       "      <td>Too $hort</td>\n",
       "      <td>actor</td>\n",
       "      <td>Himself</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Menace II Society</td>\n",
       "      <td>1993</td>\n",
       "      <td>Too $hort</td>\n",
       "      <td>actor</td>\n",
       "      <td>Lew-Loc</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Porndogs: The Adventures of Sadie</td>\n",
       "      <td>2009</td>\n",
       "      <td>Too $hort</td>\n",
       "      <td>actor</td>\n",
       "      <td>Bosco</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stop Pepper Palmer</td>\n",
       "      <td>2014</td>\n",
       "      <td>Too $hort</td>\n",
       "      <td>actor</td>\n",
       "      <td>Himself</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               title  year       name   type character   n\n",
       "0                    Suuri illusioni  1985     Homo $  actor    Guests  22\n",
       "1     Gangsta Rap: The Glockumentary  2007  Too $hort  actor   Himself NaN\n",
       "2                  Menace II Society  1993  Too $hort  actor   Lew-Loc  27\n",
       "3  Porndogs: The Adventures of Sadie  2009  Too $hort  actor     Bosco   3\n",
       "4                 Stop Pepper Palmer  2014  Too $hort  actor   Himself NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast = pd.read_csv('data/cast.csv')\n",
    "cast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>character</th>\n",
       "      <th>Batman</th>\n",
       "      <th>Superman</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1938</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "character  Batman  Superman\n",
       "year                       \n",
       "1938            1         0\n",
       "1940            1         0\n",
       "1943            1         0\n",
       "1948            0         1\n",
       "1949            2         0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a year as a \"Superman year\"\n",
    "# whose films feature more Superman characters than Batman.\n",
    "# How many years in film history have been Superman years?\n",
    "\n",
    "c = cast\n",
    "c = c[(c.character == 'Superman') | (c.character == 'Batman')]\n",
    "c = c.groupby(['year', 'character']).size()\n",
    "c = c.unstack()\n",
    "c = c.fillna(0)\n",
    "c.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Superman years:\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "d = c.Superman - c.Batman\n",
    "print('Superman years:')\n",
    "print(len(d[d > 0.0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batman years:\n",
      "24\n"
     ]
    }
   ],
   "source": [
    "# How many years have been \"Batman years\",\n",
    "# with more Batman characters than Superman characters?\n",
    "\n",
    "print('Batman years:')\n",
    "print(len(d[d < 0.0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f90845d4748>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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HUzX+mTpO/UhsCNxnxgYSbcAWZnEDjurbOBLY14wjBqzc6/X1/V9u2QWAjuM4TWIDws6p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\nHE6zkZhE2OHvg83ui5NPVNK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E2CDnQYJR2EfSg7Ha6sBbgT8BJ0s6OJZvTEyUB6wkZDR1\nnNzhhsNxkvMTwtap6xFGIHsC3zaziyorSZoSz+1iZssk3QmMiKeXmSeKc3KKu6ocJzm/JGQgnUzY\nTvRW4JiYUhxJG8YU5GsCL0ajsSUey3AKgo84HCchZtYl6Q6CUTBghqStgLvDVgYsBY4gGJXjJM0h\npCK/u7KZQe6246SGp1V3nITEoPhfgUPM7Ilm98dxBht3VTlOAiRNBOYBv3Oj4bQqPuJwHMdxEuEj\nDsdxHCcRbjgcx3GcRLjhcBzHcRLhhsNxHMdJhBsOx3EcJxFuOBzHcZxE/H+eJ8pGV3+E3AAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f90845d08d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the number of actor roles each year\n",
    "# and the number of actress roles each year\n",
    "# over the history of film.\n",
    "\n",
    "c = cast\n",
    "c = c.groupby(['year', 'type']).size()\n",
    "c = c.unstack('type')\n",
    "c.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9099ef7dd8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hsQ9UKMZRtOGQNFjSXZKelrRQ0nRJQyU9LGmxpIckDc7Jf6WkJZIWSTolJ32a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\nheMRC4UJ1r4hrFm0dUxY7bTvelBzKKx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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9099efbef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the number of actor roles each year\n",
    "# and the number of actress roles each year,\n",
    "# but this time as a kind='area' plot.\n",
    "\n",
    "c = cast\n",
    "c = c.groupby(['year', 'type']).size()\n",
    "c = c.unstack('type')\n",
    "c.plot(kind='area')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9099e62fd0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DRwHz8/F3Sb+a39tEuXcA37a5xGZxPzqITIxu6jY16Xdsmorr2RW4q9VCadVU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\nIAiGJJxEEARBMCT/H29+geb744nyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9099e34ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the difference between the number of actor roles each year\n",
    "# and the number of actress roles each year over the history of film.\n",
    "\n",
    "c = cast\n",
    "c = c.groupby(['year', 'type']).size()\n",
    "c = c.unstack('type')\n",
    "(c.actor - c.actress).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9099df4128>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D3XC1OK9fv2n9gDdi9cpyMiFpfeDnwP4RcX9eXelMA+TZPW/aDni/pG+Q3v4/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\nZmYFc5E3MyvY/wfqMbEDevIYogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9099dabdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the fraction of roles that have been 'actor' roles\n",
    "# each year in the history of film.\n",
    "\n",
    "c = cast\n",
    "c = c.groupby(['year', 'type']).size()\n",
    "c = c.unstack('type')\n",
    "(c.actor / (c.actor + c.actress)).plot(ylim=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9099d54860>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MMLOxZbs9CWzm7msAPwR+0+6CiojIPDVz3mb2KeBYdx8fl48CcPcTquy/FDDV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9099d59da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the fraction of supporting (n=2) roles\n",
    "# that have been 'actor' roles\n",
    "# each year in the history of film.\n",
    "\n",
    "c = cast\n",
    "c = c[c.n == 2]\n",
    "c = c.groupby(['year', 'type']).size()\n",
    "c = c.unstack('type')\n",
    "(c.actor / (c.actor + c.actress)).plot(ylim=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9099cc54e0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L8Vcs3Zl5IzQC47AY5ybMmhyc1KYX8B5hUApIWW29ksqeZoSYKDseOYk993i4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\ny76e/Yf1LBvUt3wum5PNst8QGKeq41V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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9099ccbac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build a plot with a line for each rank n=1 through n=3,\n",
    "# where the line shows what fraction of that rank's roles\n",
    "# were 'actor' roles for each year in the history of film.\n",
    "\n",
    "c = cast\n",
    "c = c[c.n <= 3]\n",
    "c = c.groupby(['year', 'type', 'n']).size()\n",
    "c = c.unstack('type').fillna(0)\n",
    "r = c.actor / (c.actor + c.actress)\n",
    "r = r.unstack('n')\n",
    "r.plot(ylim=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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